Overview

Dataset statistics

Number of variables28
Number of observations1386017
Missing cells428999
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.8 MiB
Average record size in memory220.0 B

Variable types

Text10
Numeric12
DateTime1
Categorical5

Alerts

KZBEW is highly imbalanced (68.3%)Imbalance
KZBEW has 419772 (30.3%) missing valuesMissing
LIFNR has unique valuesUnique
KUNNR has unique valuesUnique
VBELN has unique valuesUnique
KDAUF has unique valuesUnique
MBLNR has unique valuesUnique

Reproduction

Analysis started2024-05-21 17:05:18.863624
Analysis finished2024-05-21 17:09:19.945806
Duration4 minutes and 1.08 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

WERKS
Text

Distinct540
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:20.650808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5544068
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT86
2nd rowGE37
3rd rowES19
4th rowGE44
5th rowGE72
ValueCountFrequency (%)
ge68 2819
 
0.2%
es40 2761
 
0.2%
ir69 2758
 
0.2%
ge65 2733
 
0.2%
it66 2729
 
0.2%
ch46 2719
 
0.2%
ch11 2715
 
0.2%
ir64 2714
 
0.2%
es31 2710
 
0.2%
ir82 2710
 
0.2%
Other values (530) 1358649
98.0%
2024-05-21T19:09:21.569773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 462480
 
8.3%
E 462017
 
8.3%
R 461817
 
8.3%
6 294209
 
5.3%
4 293811
 
5.3%
3 293424
 
5.3%
2 293194
 
5.3%
9 292469
 
5.3%
1 291894
 
5.3%
8 291715
 
5.3%
Other values (9) 2107038
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 462480
 
8.3%
E 462017
 
8.3%
R 461817
 
8.3%
6 294209
 
5.3%
4 293811
 
5.3%
3 293424
 
5.3%
2 293194
 
5.3%
9 292469
 
5.3%
1 291894
 
5.3%
8 291715
 
5.3%
Other values (9) 2107038
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 462480
 
8.3%
E 462017
 
8.3%
R 461817
 
8.3%
6 294209
 
5.3%
4 293811
 
5.3%
3 293424
 
5.3%
2 293194
 
5.3%
9 292469
 
5.3%
1 291894
 
5.3%
8 291715
 
5.3%
Other values (9) 2107038
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 462480
 
8.3%
E 462017
 
8.3%
R 461817
 
8.3%
6 294209
 
5.3%
4 293811
 
5.3%
3 293424
 
5.3%
2 293194
 
5.3%
9 292469
 
5.3%
1 291894
 
5.3%
8 291715
 
5.3%
Other values (9) 2107038
38.0%
Distinct609600
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:22.343816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26141 ?
Unique (%)1.9%

Sample

1st row6203967322
2nd row2894714588
3rd row3685622318
4th rowF864030626
5th row3272320428
ValueCountFrequency (%)
3442001937 7
 
< 0.1%
3425123895 6
 
< 0.1%
f606639331 6
 
< 0.1%
3463498030 6
 
< 0.1%
3054550203 6
 
< 0.1%
3736330388 6
 
< 0.1%
f514874841 6
 
< 0.1%
8405556525 5
 
< 0.1%
f101597738 5
 
< 0.1%
3533823772 5
 
< 0.1%
Other values (609590) 1385959
> 99.9%
2024-05-21T19:09:23.237407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1746874
12.6%
0 1298880
9.4%
8 1297310
9.4%
2 1296726
9.4%
1 1296177
9.4%
5 1295799
9.3%
6 1295311
9.3%
7 1295309
9.3%
9 1295210
9.3%
4 1293543
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1746874
12.6%
0 1298880
9.4%
8 1297310
9.4%
2 1296726
9.4%
1 1296177
9.4%
5 1295799
9.3%
6 1295311
9.3%
7 1295309
9.3%
9 1295210
9.3%
4 1293543
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1746874
12.6%
0 1298880
9.4%
8 1297310
9.4%
2 1296726
9.4%
1 1296177
9.4%
5 1295799
9.3%
6 1295311
9.3%
7 1295309
9.3%
9 1295210
9.3%
4 1293543
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1746874
12.6%
0 1298880
9.4%
8 1297310
9.4%
2 1296726
9.4%
1 1296177
9.4%
5 1295799
9.3%
6 1295311
9.3%
7 1295309
9.3%
9 1295210
9.3%
4 1293543
9.3%

CHARG
Text

Distinct609658
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:23.954808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26144 ?
Unique (%)1.9%

Sample

1st rowAJ27510854
2nd rowNJ01596983
3rd rowJK94930228
4th rowSZ84312775
5th rowEH14468970
ValueCountFrequency (%)
go20175606 7
 
< 0.1%
qd07699747 6
 
< 0.1%
rb50498991 6
 
< 0.1%
rv05504392 6
 
< 0.1%
zk97386963 5
 
< 0.1%
um18170231 4
 
< 0.1%
ou41942872 4
 
< 0.1%
gj93028696 4
 
< 0.1%
sh14788826 4
 
< 0.1%
ub41774409 4
 
< 0.1%
Other values (609648) 1385967
> 99.9%
2024-05-21T19:09:24.870667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1110832
8.0%
8 1110381
8.0%
4 1109996
8.0%
9 1109267
8.0%
0 1108960
8.0%
6 1108468
 
8.0%
7 1108289
 
8.0%
2 1107809
 
8.0%
1 1107226
 
8.0%
5 1106908
 
8.0%
Other values (26) 2772034
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1110832
8.0%
8 1110381
8.0%
4 1109996
8.0%
9 1109267
8.0%
0 1108960
8.0%
6 1108468
 
8.0%
7 1108289
 
8.0%
2 1107809
 
8.0%
1 1107226
 
8.0%
5 1106908
 
8.0%
Other values (26) 2772034
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1110832
8.0%
8 1110381
8.0%
4 1109996
8.0%
9 1109267
8.0%
0 1108960
8.0%
6 1108468
 
8.0%
7 1108289
 
8.0%
2 1107809
 
8.0%
1 1107226
 
8.0%
5 1106908
 
8.0%
Other values (26) 2772034
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1110832
8.0%
8 1110381
8.0%
4 1109996
8.0%
9 1109267
8.0%
0 1108960
8.0%
6 1108468
 
8.0%
7 1108289
 
8.0%
2 1107809
 
8.0%
1 1107226
 
8.0%
5 1106908
 
8.0%
Other values (26) 2772034
20.0%

AUFNR
Real number (ℝ)

Distinct1226969
Distinct (%)89.1%
Missing9227
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4.9982652 × 1011
Minimum710870
Maximum9.9999998 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:25.097113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum710870
5-th percentile5.0088706 × 1010
Q12.5016969 × 1011
median4.9939924 × 1011
Q37.4939143 × 1011
95-th percentile9.4983139 × 1011
Maximum9.9999998 × 1011
Range9.9999927 × 1011
Interquartile range (IQR)4.9922174 × 1011

Descriptive statistics

Standard deviation2.8839108 × 1011
Coefficient of variation (CV)0.57698236
Kurtosis-1.1979527
Mean4.9982652 × 1011
Median Absolute Deviation (MAD)2.4960581 × 1011
Skewness0.0016609252
Sum6.8815615 × 1017
Variance8.3169418 × 1022
MonotonicityNot monotonic
2024-05-21T19:09:25.306699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.159474943 × 10112
 
< 0.1%
3.107406928 × 10112
 
< 0.1%
5.762672715 × 10112
 
< 0.1%
5.291236604 × 10112
 
< 0.1%
3.941249487 × 10112
 
< 0.1%
7.530245892 × 10112
 
< 0.1%
8.843114838 × 10102
 
< 0.1%
8.807363605 × 10112
 
< 0.1%
2.746275031 × 10112
 
< 0.1%
8.856598146 × 10112
 
< 0.1%
Other values (1226959) 1376770
99.3%
(Missing) 9227
 
0.7%
ValueCountFrequency (%)
710870 1
< 0.1%
3768542 1
< 0.1%
4875162 2
< 0.1%
5959148 1
< 0.1%
6366813 1
< 0.1%
6447455 2
< 0.1%
7301523 1
< 0.1%
7815979 1
< 0.1%
7968690 1
< 0.1%
8370855 1
< 0.1%
ValueCountFrequency (%)
9.999999816 × 10111
< 0.1%
9.999990643 × 10111
< 0.1%
9.999986226 × 10111
< 0.1%
9.999975752 × 10111
< 0.1%
9.999969794 × 10111
< 0.1%
9.999963768 × 10111
< 0.1%
9.999935121 × 10112
< 0.1%
9.999895699 × 10111
< 0.1%
9.999883555 × 10111
< 0.1%
9.999882435 × 10111
< 0.1%

AUFPS
Real number (ℝ)

Distinct1001
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.06466
Minimum0
Maximum1000
Zeros1317
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:25.507573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median500
Q3750
95-th percentile950
Maximum1000
Range1000
Interquartile range (IQR)500

Descriptive statistics

Standard deviation288.88164
Coefficient of variation (CV)0.57768858
Kurtosis-1.199588
Mean500.06466
Median Absolute Deviation (MAD)250
Skewness0.00022945918
Sum6.9309812 × 108
Variance83452.604
MonotonicityNot monotonic
2024-05-21T19:09:25.717559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
747 1508
 
0.1%
606 1494
 
0.1%
427 1494
 
0.1%
208 1493
 
0.1%
705 1486
 
0.1%
619 1486
 
0.1%
21 1483
 
0.1%
759 1482
 
0.1%
236 1481
 
0.1%
165 1478
 
0.1%
Other values (991) 1371132
98.9%
ValueCountFrequency (%)
0 1317
0.1%
1 1351
0.1%
2 1378
0.1%
3 1393
0.1%
4 1378
0.1%
5 1389
0.1%
6 1362
0.1%
7 1373
0.1%
8 1390
0.1%
9 1416
0.1%
ValueCountFrequency (%)
1000 1437
0.1%
999 1448
0.1%
998 1367
0.1%
997 1432
0.1%
996 1329
0.1%
995 1359
0.1%
994 1355
0.1%
993 1410
0.1%
992 1397
0.1%
991 1373
0.1%

LIFNR
Text

UNIQUE 

Distinct1386017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:26.857561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386017 ?
Unique (%)100.0%

Sample

1st rowSJMODDQNON
2nd rowLAAKCWFALZ
3rd rowLTMZJHDVRB
4th rowVZVIHIOENR
5th rowLBIUGQHWLD
ValueCountFrequency (%)
sjmoddqnon 1
 
< 0.1%
qxeddczyia 1
 
< 0.1%
eiohkzcdcb 1
 
< 0.1%
ycmvwsjgax 1
 
< 0.1%
msjipttcqi 1
 
< 0.1%
itnesyzard 1
 
< 0.1%
hvzpxjdncp 1
 
< 0.1%
qcxhbiulag 1
 
< 0.1%
estrtvyczl 1
 
< 0.1%
iqvwdakuwi 1
 
< 0.1%
Other values (1386007) 1386007
> 99.9%
2024-05-21T19:09:28.169500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Z 534662
 
3.9%
B 534500
 
3.9%
W 534454
 
3.9%
J 533718
 
3.9%
G 533485
 
3.8%
K 533440
 
3.8%
V 533434
 
3.8%
M 533355
 
3.8%
F 533290
 
3.8%
C 533265
 
3.8%
Other values (16) 8522567
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Z 534662
 
3.9%
B 534500
 
3.9%
W 534454
 
3.9%
J 533718
 
3.9%
G 533485
 
3.8%
K 533440
 
3.8%
V 533434
 
3.8%
M 533355
 
3.8%
F 533290
 
3.8%
C 533265
 
3.8%
Other values (16) 8522567
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Z 534662
 
3.9%
B 534500
 
3.9%
W 534454
 
3.9%
J 533718
 
3.9%
G 533485
 
3.8%
K 533440
 
3.8%
V 533434
 
3.8%
M 533355
 
3.8%
F 533290
 
3.8%
C 533265
 
3.8%
Other values (16) 8522567
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Z 534662
 
3.9%
B 534500
 
3.9%
W 534454
 
3.9%
J 533718
 
3.9%
G 533485
 
3.8%
K 533440
 
3.8%
V 533434
 
3.8%
M 533355
 
3.8%
F 533290
 
3.8%
C 533265
 
3.8%
Other values (16) 8522567
61.5%

LICHA
Text

Distinct1376828
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:29.220547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.966714
Min length10

Characters and Unicode

Total characters20744120
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1376822 ?
Unique (%)99.3%

Sample

1st rowCLFPVLDTSZSLVIA
2nd rowWMHCWNXSMUWNNXL
3rd rowESMITIBQXXBXASA
4th rowCIKYKSRCGEOJKUS
5th rowDNXRLSNKXCBOIVN
ValueCountFrequency (%)
sb63739883 1582
 
0.1%
rb50498991 1571
 
0.1%
go20175606 1549
 
0.1%
xf86702491 1549
 
0.1%
qd07699747 1482
 
0.1%
rv05504392 1462
 
0.1%
hbkfnktlzerbrjf 1
 
< 0.1%
dnxrlsnkxcboivn 1
 
< 0.1%
lcpnnueulgvaxyx 1
 
< 0.1%
armrjwibtjdqjha 1
 
< 0.1%
Other values (1376818) 1376818
99.3%
2024-05-21T19:09:30.435451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 797216
 
3.8%
B 797108
 
3.8%
S 796948
 
3.8%
F 796797
 
3.8%
O 796741
 
3.8%
Q 796608
 
3.8%
X 796555
 
3.8%
V 796463
 
3.8%
N 795747
 
3.8%
D 795732
 
3.8%
Other values (26) 12778205
61.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20744120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 797216
 
3.8%
B 797108
 
3.8%
S 796948
 
3.8%
F 796797
 
3.8%
O 796741
 
3.8%
Q 796608
 
3.8%
X 796555
 
3.8%
V 796463
 
3.8%
N 795747
 
3.8%
D 795732
 
3.8%
Other values (26) 12778205
61.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20744120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 797216
 
3.8%
B 797108
 
3.8%
S 796948
 
3.8%
F 796797
 
3.8%
O 796741
 
3.8%
Q 796608
 
3.8%
X 796555
 
3.8%
V 796463
 
3.8%
N 795747
 
3.8%
D 795732
 
3.8%
Other values (26) 12778205
61.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20744120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 797216
 
3.8%
B 797108
 
3.8%
S 796948
 
3.8%
F 796797
 
3.8%
O 796741
 
3.8%
Q 796608
 
3.8%
X 796555
 
3.8%
V 796463
 
3.8%
N 795747
 
3.8%
D 795732
 
3.8%
Other values (26) 12778205
61.6%

KUNNR
Text

UNIQUE 

Distinct1386017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:31.640203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386017 ?
Unique (%)100.0%

Sample

1st rowJYQJJAQFZB
2nd rowDBAGMKWZVC
3rd rowUGXNURTVTV
4th rowITZGQEBDFC
5th rowCMUOMWTIAX
ValueCountFrequency (%)
jyqjjaqfzb 1
 
< 0.1%
zjkdzqmldk 1
 
< 0.1%
fzvtqibcqe 1
 
< 0.1%
euxprptdai 1
 
< 0.1%
mahuhhqbvw 1
 
< 0.1%
iluynhnehl 1
 
< 0.1%
hewhndamjg 1
 
< 0.1%
fulujpsnwd 1
 
< 0.1%
ortdvmcfjn 1
 
< 0.1%
tcqaidzlui 1
 
< 0.1%
Other values (1386007) 1386007
> 99.9%
2024-05-21T19:09:33.056238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 534470
 
3.9%
V 534388
 
3.9%
J 534095
 
3.9%
A 533972
 
3.9%
O 533901
 
3.9%
X 533893
 
3.9%
E 533511
 
3.8%
T 533380
 
3.8%
F 533379
 
3.8%
Y 533250
 
3.8%
Other values (16) 8521931
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 534470
 
3.9%
V 534388
 
3.9%
J 534095
 
3.9%
A 533972
 
3.9%
O 533901
 
3.9%
X 533893
 
3.9%
E 533511
 
3.8%
T 533380
 
3.8%
F 533379
 
3.8%
Y 533250
 
3.8%
Other values (16) 8521931
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 534470
 
3.9%
V 534388
 
3.9%
J 534095
 
3.9%
A 533972
 
3.9%
O 533901
 
3.9%
X 533893
 
3.9%
E 533511
 
3.8%
T 533380
 
3.8%
F 533379
 
3.8%
Y 533250
 
3.8%
Other values (16) 8521931
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 534470
 
3.9%
V 534388
 
3.9%
J 534095
 
3.9%
A 533972
 
3.9%
O 533901
 
3.9%
X 533893
 
3.9%
E 533511
 
3.8%
T 533380
 
3.8%
F 533379
 
3.8%
Y 533250
 
3.8%
Other values (16) 8521931
61.5%

VBELN
Text

UNIQUE 

Distinct1386017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:34.220237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386017 ?
Unique (%)100.0%

Sample

1st rowYDABDFJXZZ
2nd rowKNQKWFZWFV
3rd rowFRYPSDDCUA
4th rowHVJMWLIVIH
5th rowITBMKFCHFJ
ValueCountFrequency (%)
ydabdfjxzz 1
 
< 0.1%
zokbumzenm 1
 
< 0.1%
oxpxhnvzbg 1
 
< 0.1%
gvezafjatx 1
 
< 0.1%
mnvzuxuifd 1
 
< 0.1%
pvuuovujar 1
 
< 0.1%
bnemvoskeg 1
 
< 0.1%
efxdlotcwx 1
 
< 0.1%
ztpzezndyo 1
 
< 0.1%
rpcgztxgkl 1
 
< 0.1%
Other values (1386007) 1386007
> 99.9%
2024-05-21T19:09:35.538239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 534705
 
3.9%
P 534579
 
3.9%
G 534184
 
3.9%
T 533877
 
3.9%
F 533737
 
3.9%
M 533659
 
3.9%
C 533625
 
3.9%
A 533567
 
3.8%
H 533483
 
3.8%
D 533237
 
3.8%
Other values (16) 8521517
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 534705
 
3.9%
P 534579
 
3.9%
G 534184
 
3.9%
T 533877
 
3.9%
F 533737
 
3.9%
M 533659
 
3.9%
C 533625
 
3.9%
A 533567
 
3.8%
H 533483
 
3.8%
D 533237
 
3.8%
Other values (16) 8521517
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 534705
 
3.9%
P 534579
 
3.9%
G 534184
 
3.9%
T 533877
 
3.9%
F 533737
 
3.9%
M 533659
 
3.9%
C 533625
 
3.9%
A 533567
 
3.8%
H 533483
 
3.8%
D 533237
 
3.8%
Other values (16) 8521517
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 534705
 
3.9%
P 534579
 
3.9%
G 534184
 
3.9%
T 533877
 
3.9%
F 533737
 
3.9%
M 533659
 
3.9%
C 533625
 
3.9%
A 533567
 
3.8%
H 533483
 
3.8%
D 533237
 
3.8%
Other values (16) 8521517
61.5%

POSNR
Real number (ℝ)

Distinct100001
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50010.177
Minimum0
Maximum100000
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:35.746235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4984
Q125007
median50004
Q375021
95-th percentile95006
Maximum100000
Range100000
Interquartile range (IQR)50014

Descriptive statistics

Standard deviation28868.728
Coefficient of variation (CV)0.57725705
Kurtosis-1.1994065
Mean50010.177
Median Absolute Deviation (MAD)25007
Skewness-0.0007367821
Sum6.9314956 × 1010
Variance8.3340344 × 108
MonotonicityNot monotonic
2024-05-21T19:09:35.943938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8707 33
 
< 0.1%
40527 33
 
< 0.1%
38722 32
 
< 0.1%
84718 32
 
< 0.1%
66455 32
 
< 0.1%
98790 32
 
< 0.1%
98736 32
 
< 0.1%
33732 31
 
< 0.1%
62271 31
 
< 0.1%
61415 31
 
< 0.1%
Other values (99991) 1385698
> 99.9%
ValueCountFrequency (%)
0 17
< 0.1%
1 13
< 0.1%
2 8
 
< 0.1%
3 8
 
< 0.1%
4 14
< 0.1%
5 10
< 0.1%
6 13
< 0.1%
7 21
< 0.1%
8 16
< 0.1%
9 17
< 0.1%
ValueCountFrequency (%)
100000 14
< 0.1%
99999 9
< 0.1%
99998 14
< 0.1%
99997 8
< 0.1%
99996 11
< 0.1%
99995 17
< 0.1%
99994 16
< 0.1%
99993 12
< 0.1%
99992 10
< 0.1%
99991 18
< 0.1%

BUDAT
Date

Distinct1461
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
Minimum2019-01-01 00:00:00
Maximum2022-12-31 00:00:00
2024-05-21T19:09:36.140971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:36.359160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ZEILE
Real number (ℝ)

Distinct1001
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.25994
Minimum0
Maximum1000
Zeros1352
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:36.565127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median501
Q3750
95-th percentile951
Maximum1000
Range1000
Interquartile range (IQR)500

Descriptive statistics

Standard deviation288.99075
Coefficient of variation (CV)0.57768118
Kurtosis-1.1996508
Mean500.25994
Median Absolute Deviation (MAD)250
Skewness-0.001383029
Sum6.9336879 × 108
Variance83515.656
MonotonicityNot monotonic
2024-05-21T19:09:36.767124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
972 1491
 
0.1%
188 1490
 
0.1%
725 1475
 
0.1%
684 1475
 
0.1%
717 1475
 
0.1%
982 1474
 
0.1%
625 1473
 
0.1%
756 1469
 
0.1%
198 1468
 
0.1%
328 1468
 
0.1%
Other values (991) 1371259
98.9%
ValueCountFrequency (%)
0 1352
0.1%
1 1430
0.1%
2 1366
0.1%
3 1321
0.1%
4 1357
0.1%
5 1339
0.1%
6 1389
0.1%
7 1360
0.1%
8 1380
0.1%
9 1418
0.1%
ValueCountFrequency (%)
1000 1403
0.1%
999 1420
0.1%
998 1418
0.1%
997 1327
0.1%
996 1404
0.1%
995 1415
0.1%
994 1427
0.1%
993 1359
0.1%
992 1317
0.1%
991 1386
0.1%

BWART
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.59298
Minimum101
Maximum802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:36.930437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1261
median261
Q3501
95-th percentile801
Maximum802
Range701
Interquartile range (IQR)240

Descriptive statistics

Standard deviation194.41921
Coefficient of variation (CV)0.55454392
Kurtosis-0.068577587
Mean350.59298
Median Absolute Deviation (MAD)1
Skewness0.99834569
Sum4.8592784 × 108
Variance37798.828
MonotonicityNot monotonic
2024-05-21T19:09:37.089407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
261 667644
48.2%
601 80016
 
5.8%
262 79970
 
5.8%
101 79803
 
5.8%
602 79788
 
5.8%
102 79300
 
5.7%
302 45807
 
3.3%
701 45746
 
3.3%
802 45718
 
3.3%
301 45645
 
3.3%
Other values (3) 136580
 
9.9%
ValueCountFrequency (%)
101 79803
 
5.8%
102 79300
 
5.7%
261 667644
48.2%
262 79970
 
5.8%
301 45645
 
3.3%
302 45807
 
3.3%
501 45613
 
3.3%
502 45396
 
3.3%
601 80016
 
5.8%
602 79788
 
5.8%
ValueCountFrequency (%)
802 45718
3.3%
801 45571
3.3%
701 45746
3.3%
602 79788
5.8%
601 80016
5.8%
502 45396
3.3%
501 45613
3.3%
302 45807
3.3%
301 45645
3.3%
262 79970
5.8%

KZBEW
Categorical

IMBALANCE  MISSING 

Distinct26
Distinct (%)< 0.1%
Missing419772
Missing (%)30.3%
Memory size21.1 MiB
F
517844 
B
317857 
L
121908 
N
 
412
T
 
405
Other values (21)
 
7819

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters966245
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowB
3rd rowF
4th rowF
5th rowL

Common Values

ValueCountFrequency (%)
F 517844
37.4%
B 317857
22.9%
L 121908
 
8.8%
N 412
 
< 0.1%
T 405
 
< 0.1%
Q 393
 
< 0.1%
J 392
 
< 0.1%
Z 388
 
< 0.1%
O 383
 
< 0.1%
K 381
 
< 0.1%
Other values (16) 5882
 
0.4%
(Missing) 419772
30.3%

Length

2024-05-21T19:09:37.260404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f 517844
53.6%
b 317857
32.9%
l 121908
 
12.6%
n 412
 
< 0.1%
t 405
 
< 0.1%
q 393
 
< 0.1%
j 392
 
< 0.1%
z 388
 
< 0.1%
o 383
 
< 0.1%
k 381
 
< 0.1%
Other values (16) 5882
 
0.6%

Most occurring characters

ValueCountFrequency (%)
F 517844
53.6%
B 317857
32.9%
L 121908
 
12.6%
N 412
 
< 0.1%
T 405
 
< 0.1%
Q 393
 
< 0.1%
J 392
 
< 0.1%
Z 388
 
< 0.1%
O 383
 
< 0.1%
K 381
 
< 0.1%
Other values (16) 5882
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 966245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 517844
53.6%
B 317857
32.9%
L 121908
 
12.6%
N 412
 
< 0.1%
T 405
 
< 0.1%
Q 393
 
< 0.1%
J 392
 
< 0.1%
Z 388
 
< 0.1%
O 383
 
< 0.1%
K 381
 
< 0.1%
Other values (16) 5882
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 966245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 517844
53.6%
B 317857
32.9%
L 121908
 
12.6%
N 412
 
< 0.1%
T 405
 
< 0.1%
Q 393
 
< 0.1%
J 392
 
< 0.1%
Z 388
 
< 0.1%
O 383
 
< 0.1%
K 381
 
< 0.1%
Other values (16) 5882
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 966245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 517844
53.6%
B 317857
32.9%
L 121908
 
12.6%
N 412
 
< 0.1%
T 405
 
< 0.1%
Q 393
 
< 0.1%
J 392
 
< 0.1%
Z 388
 
< 0.1%
O 383
 
< 0.1%
K 381
 
< 0.1%
Other values (16) 5882
 
0.6%

MENGE
Real number (ℝ)

Distinct1186172
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5035
Minimum1
Maximum2499.9927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:37.434438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile127.10534
Q1627.1471
median1251.9352
Q31875.8413
95-th percentile2375.607
Maximum2499.9927
Range2498.9927
Interquartile range (IQR)1248.6942

Descriptive statistics

Standard deviation721.06956
Coefficient of variation (CV)0.57616263
Kurtosis-1.1994999
Mean1251.5035
Median Absolute Deviation (MAD)624.4352
Skewness-0.00080500476
Sum1.7346052 × 109
Variance519941.32
MonotonicityNot monotonic
2024-05-21T19:09:37.634438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2329.3 19
 
< 0.1%
982.5 18
 
< 0.1%
2339.7 18
 
< 0.1%
1165.2 18
 
< 0.1%
1119.2 17
 
< 0.1%
2487.3 17
 
< 0.1%
2340.6 17
 
< 0.1%
2433.6 17
 
< 0.1%
2269.5 16
 
< 0.1%
1091.9 16
 
< 0.1%
Other values (1186162) 1385844
> 99.9%
ValueCountFrequency (%)
1 4
< 0.1%
1.1 4
< 0.1%
1.100399 1
 
< 0.1%
1.1004 1
 
< 0.1%
1.1072067 1
 
< 0.1%
1.1099707 1
 
< 0.1%
1.11084 1
 
< 0.1%
1.1110698 1
 
< 0.1%
1.11234 1
 
< 0.1%
1.113295 1
 
< 0.1%
ValueCountFrequency (%)
2499.9927 1
< 0.1%
2499.991 1
< 0.1%
2499.9902 1
< 0.1%
2499.9878 1
< 0.1%
2499.9832 1
< 0.1%
2499.983 1
< 0.1%
2499.9807 1
< 0.1%
2499.979 1
< 0.1%
2499.9758 1
< 0.1%
2499.9695 1
< 0.1%

MEINS
Real number (ℝ)

Distinct1220242
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250.8574
Minimum1
Maximum2499.9932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:37.832125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile126.255
Q1626.50118
median1251.186
Q31875.8765
95-th percentile2374.7527
Maximum2499.9932
Range2498.9932
Interquartile range (IQR)1249.3753

Descriptive statistics

Standard deviation721.34712
Coefficient of variation (CV)0.57668215
Kurtosis-1.2002612
Mean1250.8574
Median Absolute Deviation (MAD)624.68604
Skewness-0.0006710537
Sum1.7337096 × 109
Variance520341.66
MonotonicityNot monotonic
2024-05-21T19:09:38.031110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440.5 18
 
< 0.1%
1120.5 17
 
< 0.1%
905 17
 
< 0.1%
1695.6 17
 
< 0.1%
2115.1 17
 
< 0.1%
348.1 17
 
< 0.1%
1260.2 16
 
< 0.1%
460.9 16
 
< 0.1%
1334.3 16
 
< 0.1%
890.6 16
 
< 0.1%
Other values (1220232) 1385850
> 99.9%
ValueCountFrequency (%)
1 7
< 0.1%
1.1 8
< 0.1%
1.1003222 1
 
< 0.1%
1.104957276 1
 
< 0.1%
1.105 1
 
< 0.1%
1.107477 1
 
< 0.1%
1.11 1
 
< 0.1%
1.110523968 1
 
< 0.1%
1.113243 1
 
< 0.1%
1.1135 1
 
< 0.1%
ValueCountFrequency (%)
2499.993192 1
< 0.1%
2499.99 2
< 0.1%
2499.98618 1
< 0.1%
2499.984874 1
< 0.1%
2499.981849 1
< 0.1%
2499.981 1
< 0.1%
2499.979034 1
< 0.1%
2499.978294 1
< 0.1%
2499.978 1
< 0.1%
2499.973987 1
< 0.1%

EBELN
Text

Distinct1340597
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:39.205073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1304202 ?
Unique (%)94.1%

Sample

1st rowTANPCWPOZR
2nd rowVMASXFCGTG
3rd rowRTVORZPBMH
4th rowDSCSCNTSLV
5th rowFERGXPYOYD
ValueCountFrequency (%)
yymrdkqylu 4
 
< 0.1%
seqgydijdh 4
 
< 0.1%
ynqmptmsnl 4
 
< 0.1%
uctjyhghwp 4
 
< 0.1%
otqhglwbkx 4
 
< 0.1%
qyssjgsdil 4
 
< 0.1%
hzmlxwgshu 4
 
< 0.1%
shjtygifkn 4
 
< 0.1%
nlsolqianj 4
 
< 0.1%
whbuzyprlz 4
 
< 0.1%
Other values (1340587) 1385977
> 99.9%
2024-05-21T19:09:40.536110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 534097
 
3.9%
J 534073
 
3.9%
O 533861
 
3.9%
B 533840
 
3.9%
G 533815
 
3.9%
W 533760
 
3.9%
V 533657
 
3.9%
T 533603
 
3.8%
K 533429
 
3.8%
R 533339
 
3.8%
Other values (16) 8522696
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 534097
 
3.9%
J 534073
 
3.9%
O 533861
 
3.9%
B 533840
 
3.9%
G 533815
 
3.9%
W 533760
 
3.9%
V 533657
 
3.9%
T 533603
 
3.8%
K 533429
 
3.8%
R 533339
 
3.8%
Other values (16) 8522696
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 534097
 
3.9%
J 534073
 
3.9%
O 533861
 
3.9%
B 533840
 
3.9%
G 533815
 
3.9%
W 533760
 
3.9%
V 533657
 
3.9%
T 533603
 
3.8%
K 533429
 
3.8%
R 533339
 
3.8%
Other values (16) 8522696
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 534097
 
3.9%
J 534073
 
3.9%
O 533861
 
3.9%
B 533840
 
3.9%
G 533815
 
3.9%
W 533760
 
3.9%
V 533657
 
3.9%
T 533603
 
3.8%
K 533429
 
3.8%
R 533339
 
3.8%
Other values (16) 8522696
61.5%

EBELP
Real number (ℝ)

Distinct10001
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5001.9543
Minimum0
Maximum10000
Zeros131
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:40.750077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile501
Q12505
median5001
Q37499
95-th percentile9501
Maximum10000
Range10000
Interquartile range (IQR)4994

Descriptive statistics

Standard deviation2886.0302
Coefficient of variation (CV)0.57698051
Kurtosis-1.1988768
Mean5001.9543
Median Absolute Deviation (MAD)2497
Skewness7.9029056 × 10-6
Sum6.9327937 × 109
Variance8329170.1
MonotonicityNot monotonic
2024-05-21T19:09:41.037738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7042 184
 
< 0.1%
7000 182
 
< 0.1%
2131 179
 
< 0.1%
299 179
 
< 0.1%
4237 179
 
< 0.1%
2429 179
 
< 0.1%
5215 178
 
< 0.1%
5900 178
 
< 0.1%
1040 178
 
< 0.1%
2814 178
 
< 0.1%
Other values (9991) 1384223
99.9%
ValueCountFrequency (%)
0 131
< 0.1%
1 151
< 0.1%
2 125
< 0.1%
3 143
< 0.1%
4 138
< 0.1%
5 142
< 0.1%
6 150
< 0.1%
7 145
< 0.1%
8 154
< 0.1%
9 118
< 0.1%
ValueCountFrequency (%)
10000 111
< 0.1%
9999 142
< 0.1%
9998 163
< 0.1%
9997 144
< 0.1%
9996 149
< 0.1%
9995 145
< 0.1%
9994 142
< 0.1%
9993 135
< 0.1%
9992 150
< 0.1%
9991 133
< 0.1%

KDAUF
Text

UNIQUE 

Distinct1386017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:42.164641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386017 ?
Unique (%)100.0%

Sample

1st rowXHCHOAHMEX
2nd rowFFJZYLIMZX
3rd rowWFOSDPEIQU
4th rowHGQRRMBJQN
5th rowJCNCBAKPLY
ValueCountFrequency (%)
xhchoahmex 1
 
< 0.1%
bchqfeqmxz 1
 
< 0.1%
tcwixzfiuq 1
 
< 0.1%
jofwaluzzu 1
 
< 0.1%
xeypiunsua 1
 
< 0.1%
havfejhecq 1
 
< 0.1%
amjkfyscai 1
 
< 0.1%
nfcfusctav 1
 
< 0.1%
vgnymeehjo 1
 
< 0.1%
pjhhunysmb 1
 
< 0.1%
Other values (1386007) 1386007
> 99.9%
2024-05-21T19:09:43.488596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 534287
 
3.9%
P 534190
 
3.9%
V 534144
 
3.9%
R 533804
 
3.9%
O 533803
 
3.9%
Y 533555
 
3.8%
E 533496
 
3.8%
F 533440
 
3.8%
H 533234
 
3.8%
M 533201
 
3.8%
Other values (16) 8523016
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 534287
 
3.9%
P 534190
 
3.9%
V 534144
 
3.9%
R 533804
 
3.9%
O 533803
 
3.9%
Y 533555
 
3.8%
E 533496
 
3.8%
F 533440
 
3.8%
H 533234
 
3.8%
M 533201
 
3.8%
Other values (16) 8523016
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 534287
 
3.9%
P 534190
 
3.9%
V 534144
 
3.9%
R 533804
 
3.9%
O 533803
 
3.9%
Y 533555
 
3.8%
E 533496
 
3.8%
F 533440
 
3.8%
H 533234
 
3.8%
M 533201
 
3.8%
Other values (16) 8523016
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 534287
 
3.9%
P 534190
 
3.9%
V 534144
 
3.9%
R 533804
 
3.9%
O 533803
 
3.9%
Y 533555
 
3.8%
E 533496
 
3.8%
F 533440
 
3.8%
H 533234
 
3.8%
M 533201
 
3.8%
Other values (16) 8523016
61.5%

KDPOS
Real number (ℝ)

Distinct100001
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49996.39
Minimum0
Maximum100000
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:43.695599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4998
Q124971
median50009
Q375007
95-th percentile94981
Maximum100000
Range100000
Interquartile range (IQR)50036

Descriptive statistics

Standard deviation28868.696
Coefficient of variation (CV)0.57741561
Kurtosis-1.200954
Mean49996.39
Median Absolute Deviation (MAD)25017
Skewness-0.00028816558
Sum6.9295847 × 1010
Variance8.3340162 × 108
MonotonicityNot monotonic
2024-05-21T19:09:43.935086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20300 33
 
< 0.1%
97897 32
 
< 0.1%
25322 31
 
< 0.1%
93429 31
 
< 0.1%
401 30
 
< 0.1%
54749 29
 
< 0.1%
23221 29
 
< 0.1%
23185 29
 
< 0.1%
84619 29
 
< 0.1%
67322 29
 
< 0.1%
Other values (99991) 1385715
> 99.9%
ValueCountFrequency (%)
0 11
< 0.1%
1 5
 
< 0.1%
2 18
< 0.1%
3 16
< 0.1%
4 12
< 0.1%
5 11
< 0.1%
6 11
< 0.1%
7 8
< 0.1%
8 10
< 0.1%
9 13
< 0.1%
ValueCountFrequency (%)
100000 14
< 0.1%
99999 17
< 0.1%
99998 14
< 0.1%
99997 18
< 0.1%
99996 14
< 0.1%
99995 17
< 0.1%
99994 15
< 0.1%
99993 10
< 0.1%
99992 14
< 0.1%
99991 20
< 0.1%

MBLNR
Text

UNIQUE 

Distinct1386017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:45.127097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13860170
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1386017 ?
Unique (%)100.0%

Sample

1st rowOLSRTUURJN
2nd rowMABSWAGAYL
3rd rowCVQHNUWTOS
4th rowWRGJKMYRXD
5th rowULEAEOPRCL
ValueCountFrequency (%)
olsrtuurjn 1
 
< 0.1%
klcvkyhywr 1
 
< 0.1%
jypunnnhgq 1
 
< 0.1%
smhvkbdrvf 1
 
< 0.1%
zzddnqkisl 1
 
< 0.1%
aviqaudvjr 1
 
< 0.1%
pfhpjcvkoi 1
 
< 0.1%
sxdqszvqny 1
 
< 0.1%
rmiglphztl 1
 
< 0.1%
ztqpuucxjg 1
 
< 0.1%
Other values (1386007) 1386007
> 99.9%
2024-05-21T19:09:46.511951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 534565
 
3.9%
M 534507
 
3.9%
B 534084
 
3.9%
V 534035
 
3.9%
Y 533891
 
3.9%
P 533433
 
3.8%
A 533423
 
3.8%
L 533385
 
3.8%
O 533271
 
3.8%
S 533183
 
3.8%
Other values (16) 8522393
61.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 534565
 
3.9%
M 534507
 
3.9%
B 534084
 
3.9%
V 534035
 
3.9%
Y 533891
 
3.9%
P 533433
 
3.8%
A 533423
 
3.8%
L 533385
 
3.8%
O 533271
 
3.8%
S 533183
 
3.8%
Other values (16) 8522393
61.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 534565
 
3.9%
M 534507
 
3.9%
B 534084
 
3.9%
V 534035
 
3.9%
Y 533891
 
3.9%
P 533433
 
3.8%
A 533423
 
3.8%
L 533385
 
3.8%
O 533271
 
3.8%
S 533183
 
3.8%
Other values (16) 8522393
61.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13860170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 534565
 
3.9%
M 534507
 
3.9%
B 534084
 
3.9%
V 534035
 
3.9%
Y 533891
 
3.9%
P 533433
 
3.8%
A 533423
 
3.8%
L 533385
 
3.8%
O 533271
 
3.8%
S 533183
 
3.8%
Other values (16) 8522393
61.5%

MJAHR
Real number (ℝ)

Distinct1001
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.13033
Minimum0
Maximum1000
Zeros1362
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.1 MiB
2024-05-21T19:09:46.720910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median500
Q3751
95-th percentile950
Maximum1000
Range1000
Interquartile range (IQR)501

Descriptive statistics

Standard deviation288.83194
Coefficient of variation (CV)0.57751334
Kurtosis-1.1995569
Mean500.13033
Median Absolute Deviation (MAD)250
Skewness0.00054135705
Sum6.9318914 × 108
Variance83423.888
MonotonicityNot monotonic
2024-05-21T19:09:46.920943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
564 1487
 
0.1%
259 1485
 
0.1%
218 1484
 
0.1%
572 1484
 
0.1%
940 1481
 
0.1%
930 1477
 
0.1%
186 1476
 
0.1%
257 1476
 
0.1%
766 1475
 
0.1%
997 1473
 
0.1%
Other values (991) 1371219
98.9%
ValueCountFrequency (%)
0 1362
0.1%
1 1354
0.1%
2 1450
0.1%
3 1342
0.1%
4 1349
0.1%
5 1370
0.1%
6 1268
0.1%
7 1395
0.1%
8 1356
0.1%
9 1438
0.1%
ValueCountFrequency (%)
1000 1416
0.1%
999 1358
0.1%
998 1430
0.1%
997 1473
0.1%
996 1369
0.1%
995 1296
0.1%
994 1381
0.1%
993 1353
0.1%
992 1381
0.1%
991 1370
0.1%

Pais
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
GE
232163 
IT
231317 
IR
231163 
CH
230866 
FR
230654 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2772034
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT
2nd rowGE
3rd rowES
4th rowGE
5th rowGE

Common Values

ValueCountFrequency (%)
GE 232163
16.8%
IT 231317
16.7%
IR 231163
16.7%
CH 230866
16.7%
FR 230654
16.6%
ES 229854
16.6%

Length

2024-05-21T19:09:47.101516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T19:09:47.266750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ge 232163
16.8%
it 231317
16.7%
ir 231163
16.7%
ch 230866
16.7%
fr 230654
16.6%
es 229854
16.6%

Most occurring characters

ValueCountFrequency (%)
I 462480
16.7%
E 462017
16.7%
R 461817
16.7%
G 232163
8.4%
T 231317
8.3%
C 230866
8.3%
H 230866
8.3%
F 230654
8.3%
S 229854
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2772034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 462480
16.7%
E 462017
16.7%
R 461817
16.7%
G 232163
8.4%
T 231317
8.3%
C 230866
8.3%
H 230866
8.3%
F 230654
8.3%
S 229854
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2772034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 462480
16.7%
E 462017
16.7%
R 461817
16.7%
G 232163
8.4%
T 231317
8.3%
C 230866
8.3%
H 230866
8.3%
F 230654
8.3%
S 229854
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2772034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 462480
16.7%
E 462017
16.7%
R 461817
16.7%
G 232163
8.4%
T 231317
8.3%
C 230866
8.3%
H 230866
8.3%
F 230654
8.3%
S 229854
8.3%

MAKTK
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
Aluminum
171708 
Biologics
165737 
Cardboard
163005 
Chemicals
158464 
Ferrous Metal
153582 
Other values (4)
573521 

Length

Max length13
Median length9
Mean length7.7695223
Min length4

Characters and Unicode

Total characters10768690
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAluminum
2nd rowWood
3rd rowChemicals
4th rowCardboard
5th rowChemicals

Common Values

ValueCountFrequency (%)
Aluminum 171708
12.4%
Biologics 165737
12.0%
Cardboard 163005
11.8%
Chemicals 158464
11.4%
Ferrous Metal 153582
11.1%
Glass 149543
10.8%
Paper 145230
10.5%
Plastic 141583
10.2%
Wood 137165
9.9%

Length

2024-05-21T19:09:47.455965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T19:09:47.627014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
aluminum 171708
11.2%
biologics 165737
10.8%
cardboard 163005
10.6%
chemicals 158464
10.3%
ferrous 153582
10.0%
metal 153582
10.0%
glass 149543
9.7%
paper 145230
9.4%
plastic 141583
9.2%
wood 137165
8.9%

Most occurring characters

ValueCountFrequency (%)
a 1074412
 
10.0%
l 940617
 
8.7%
o 922391
 
8.6%
s 918452
 
8.5%
i 803229
 
7.5%
r 778404
 
7.2%
e 610858
 
5.7%
m 501880
 
4.7%
u 496998
 
4.6%
c 465784
 
4.3%
Other values (16) 3255665
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10768690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1074412
 
10.0%
l 940617
 
8.7%
o 922391
 
8.6%
s 918452
 
8.5%
i 803229
 
7.5%
r 778404
 
7.2%
e 610858
 
5.7%
m 501880
 
4.7%
u 496998
 
4.6%
c 465784
 
4.3%
Other values (16) 3255665
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10768690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1074412
 
10.0%
l 940617
 
8.7%
o 922391
 
8.6%
s 918452
 
8.5%
i 803229
 
7.5%
r 778404
 
7.2%
e 610858
 
5.7%
m 501880
 
4.7%
u 496998
 
4.6%
c 465784
 
4.3%
Other values (16) 3255665
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10768690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1074412
 
10.0%
l 940617
 
8.7%
o 922391
 
8.6%
s 918452
 
8.5%
i 803229
 
7.5%
r 778404
 
7.2%
e 610858
 
5.7%
m 501880
 
4.7%
u 496998
 
4.6%
c 465784
 
4.3%
Other values (16) 3255665
30.2%

Fecha_Año_Mes
Categorical

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2022-12
 
29648
2020-10
 
29630
2021-08
 
29599
2021-07
 
29587
2020-12
 
29553
Other values (43)
1238000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9702119
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01
2nd row2019-12
3rd row2020-03
4th row2019-01
5th row2021-01

Common Values

ValueCountFrequency (%)
2022-12 29648
 
2.1%
2020-10 29630
 
2.1%
2021-08 29599
 
2.1%
2021-07 29587
 
2.1%
2020-12 29553
 
2.1%
2019-07 29547
 
2.1%
2022-05 29510
 
2.1%
2020-05 29451
 
2.1%
2019-03 29448
 
2.1%
2020-08 29445
 
2.1%
Other values (38) 1090599
78.7%

Length

2024-05-21T19:09:47.841928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-12 29648
 
2.1%
2020-10 29630
 
2.1%
2021-08 29599
 
2.1%
2021-07 29587
 
2.1%
2020-12 29553
 
2.1%
2019-07 29547
 
2.1%
2022-05 29510
 
2.1%
2020-05 29451
 
2.1%
2019-03 29448
 
2.1%
2020-08 29445
 
2.1%
Other values (38) 1090599
78.7%

Most occurring characters

ValueCountFrequency (%)
2 2996694
30.9%
0 2887180
29.8%
- 1386017
14.3%
1 1274212
13.1%
9 459676
 
4.7%
7 117941
 
1.2%
5 117681
 
1.2%
8 117271
 
1.2%
3 117151
 
1.2%
4 114392
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9702119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2996694
30.9%
0 2887180
29.8%
- 1386017
14.3%
1 1274212
13.1%
9 459676
 
4.7%
7 117941
 
1.2%
5 117681
 
1.2%
8 117271
 
1.2%
3 117151
 
1.2%
4 114392
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9702119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2996694
30.9%
0 2887180
29.8%
- 1386017
14.3%
1 1274212
13.1%
9 459676
 
4.7%
7 117941
 
1.2%
5 117681
 
1.2%
8 117271
 
1.2%
3 117151
 
1.2%
4 114392
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9702119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2996694
30.9%
0 2887180
29.8%
- 1386017
14.3%
1 1274212
13.1%
9 459676
 
4.7%
7 117941
 
1.2%
5 117681
 
1.2%
8 117271
 
1.2%
3 117151
 
1.2%
4 114392
 
1.2%

Dia
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.728619
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-05-21T19:09:47.990173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8000323
Coefficient of variation (CV)0.55949172
Kurtosis-1.193748
Mean15.728619
Median Absolute Deviation (MAD)8
Skewness0.0071230224
Sum21800134
Variance77.440569
MonotonicityNot monotonic
2024-05-21T19:09:48.157223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 46056
 
3.3%
16 45848
 
3.3%
28 45793
 
3.3%
13 45666
 
3.3%
3 45640
 
3.3%
12 45622
 
3.3%
7 45608
 
3.3%
25 45593
 
3.3%
23 45593
 
3.3%
6 45584
 
3.3%
Other values (21) 929014
67.0%
ValueCountFrequency (%)
1 45539
3.3%
2 45477
3.3%
3 45640
3.3%
4 45494
3.3%
5 45519
3.3%
6 45584
3.3%
7 45608
3.3%
8 45475
3.3%
9 45580
3.3%
10 45495
3.3%
ValueCountFrequency (%)
31 26567
1.9%
30 41767
3.0%
29 42672
3.1%
28 45793
3.3%
27 45408
3.3%
26 45270
3.3%
25 45593
3.3%
24 45472
3.3%
23 45593
3.3%
22 45572
3.3%

Mes
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5296205
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 MiB
2024-05-21T19:09:48.310133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4478548
Coefficient of variation (CV)0.52803296
Kurtosis-1.2077066
Mean6.5296205
Median Absolute Deviation (MAD)3
Skewness-0.010702725
Sum9050165
Variance11.887703
MonotonicityNot monotonic
2024-05-21T19:09:48.456653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 117941
8.5%
12 117804
8.5%
5 117681
8.5%
10 117666
8.5%
8 117271
8.5%
3 117151
8.5%
1 117022
8.4%
11 114691
8.3%
4 114392
8.3%
6 113904
8.2%
Other values (2) 220494
15.9%
ValueCountFrequency (%)
1 117022
8.4%
2 106831
7.7%
3 117151
8.5%
4 114392
8.3%
5 117681
8.5%
6 113904
8.2%
7 117941
8.5%
8 117271
8.5%
9 113663
8.2%
10 117666
8.5%
ValueCountFrequency (%)
12 117804
8.5%
11 114691
8.3%
10 117666
8.5%
9 113663
8.2%
8 117271
8.5%
7 117941
8.5%
6 113904
8.2%
5 117681
8.5%
4 114392
8.3%
3 117151
8.5%

Año
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 MiB
2020
347641 
2021
346325 
2022
346038 
2019
346013 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5544068
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2020
4th row2019
5th row2021

Common Values

ValueCountFrequency (%)
2020 347641
25.1%
2021 346325
25.0%
2022 346038
25.0%
2019 346013
25.0%

Length

2024-05-21T19:09:48.618497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T19:09:48.760463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2020 347641
25.1%
2021 346325
25.0%
2022 346038
25.0%
2019 346013
25.0%

Most occurring characters

ValueCountFrequency (%)
2 2772059
50.0%
0 1733658
31.3%
1 692338
 
12.5%
9 346013
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2772059
50.0%
0 1733658
31.3%
1 692338
 
12.5%
9 346013
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2772059
50.0%
0 1733658
31.3%
1 692338
 
12.5%
9 346013
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5544068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2772059
50.0%
0 1733658
31.3%
1 692338
 
12.5%
9 346013
 
6.2%

Interactions

2024-05-21T19:09:00.244966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:03.215174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:08.780282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:13.961168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:19.203265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:24.351582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:29.527414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:34.628414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:39.709028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:45.031727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:50.136029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:55.239036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:00.655920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:03.689831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:09.207320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:14.387160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:19.628211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:24.791546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:29.959414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:35.058888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:40.136831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:45.450736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:50.560023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:55.652948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:01.079296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:04.150912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:09.638323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:14.803169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:20.054732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:25.217547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:30.368410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:35.482887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:40.656407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:45.887690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:50.982027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:56.072992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:01.490806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:04.608005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:10.078321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:15.224169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:20.467693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:25.711547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:30.785413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:35.910882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:41.132399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:46.307690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:51.413077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:56.487948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:01.933821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:05.075159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:10.535328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:15.648124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:20.917146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:26.124545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:31.206448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:36.327841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:41.601435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:46.725693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:51.837032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:56.907947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:02.355847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:05.540528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:10.972319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:16.128123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:21.342193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:26.545546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:31.625413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:36.758914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:42.066435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:47.155737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:52.269080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:57.329985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:02.765292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:06.038328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:11.392319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:16.563168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:21.771148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:26.980584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:32.051411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:37.164948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:42.488437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:47.575024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:52.680071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:57.742993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:03.186285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:06.485334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:11.827282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:17.030158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:22.203149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:27.399595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:32.507419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:37.596919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:42.913803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:48.003070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:53.096082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:58.161985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:03.588313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:06.948283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:12.249319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:17.461122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:22.643187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:27.838548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:32.927447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:38.017913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:43.326856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:48.409024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:53.518040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:58.567086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:04.013861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:07.419318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:12.673718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:17.892168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:23.075181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:28.256550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:33.372458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:38.435956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:43.753856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:48.835024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:53.941083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:58.987196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:04.426862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:07.871286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:13.098707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:18.327160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:23.479596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:28.674425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:33.778417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:38.853026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:44.164903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:49.279027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:54.373037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:59.380925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:09:04.846863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:08.316282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:13.513161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:18.755217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:23.907549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:29.095412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:34.191456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:39.264064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:44.577530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:49.691023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:54.799071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T19:08:59.800926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-21T19:09:05.941862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-21T19:09:09.326920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-21T19:09:15.274809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

WERKSNumMaterialCHARGAUFNRAUFPSLIFNRLICHAKUNNRVBELNPOSNRBUDATZEILEBWARTKZBEWMENGEMEINSEBELNEBELPKDAUFKDPOSMBLNRMJAHRPaisMAKTKFecha_Año_MesDiaMesAño
0IT866203967322AJ275108548.021277e+11669SJMODDQNONCLFPVLDTSZSLVIAJYQJJAQFZBYDABDFJXZZ472862019-01-24744301L2359.38800641.730396TANPCWPOZR3278XHCHOAHMEX25888OLSRTUURJN614ITAluminum2019-012412019
1GE372894714588NJ015969835.956571e+11914LAAKCWFALZWMHCWNXSMUWNNXLDBAGMKWZVCKNQKWFZWFV473362019-12-27307601B2168.76000480.800000VMASXFCGTG6576FFJZYLIMZX34722MABSWAGAYL89GEWood2019-1227122019
2ES193685622318JK949302284.291935e+11833LTMZJHDVRBESMITIBQXXBXASAUGXNURTVTVFRYPSDDCUA207772020-03-01703101F661.157171179.363940RTVORZPBMH95WFOSDPEIQU19862CVQHNUWTOS450ESChemicals2020-03132020
3GE44F864030626SZ843127757.914746e+10423VZVIHIOENRCIKYKSRCGEOJKUSITZGQEBDFCHVJMWLIVIH722812019-01-16892502F2494.402601913.810000DSCSCNTSLV1198HGQRRMBJQN6253WRGJKMYRXD259GECardboard2019-011612019
4GE723272320428EH144689707.551500e+11346LBIUGQHWLDDNXRLSNKXCBOIVNCMUOMWTIAXITBMKFCHFJ73922021-01-16521102L1989.84990273.311335FERGXPYOYD8249JCNCBAKPLY52584ULEAEOPRCL400GEChemicals2021-011612021
5ES689105202458QE086264552.821140e+11841EIOHKZCDCBLCPNNUEULGVAXYXFZVTQIBCQEOXPXHNVZBG395522022-07-18760302B132.57000775.330176XRYLRALBQC7080TCWIXZFIUQ59340JYPUNNNHGQ901ESCardboard2022-071872022
6GE836938911352IW605967398.495391e+11521YCMVWSJGAXARMRJWIBTJDQJHAEUXPRPTDAIGVEZAFJATX612662020-02-11644101L1680.81790355.669711GEDDTDXINK2870JOFWALUZZU82293SMHVKBDRVF564GEFerrous Metal2020-021122020
7ES883861284303GO447751383.805904e+11309MSJIPTTCQIDZJNLBRJHWNFIULMAHUHHQBVWMNVZUXUIFD393892022-12-1743102F2178.24800863.857325QEZVBKFTZY9587XEYPIUNSUA26479ZZDDNQKISL278ESPlastic2022-1217122022
8GE430737070534BT583065951.447880e+11556ITNESYZARDAYXGBXJGQNLXICNILUYNHNEHLPVUUOVUJAR874022021-09-09140602F903.248842285.500000IFOKCAEMRF7332HAVFEJHECQ90532AVIQAUDVJR198GEAluminum2021-09992021
9GE47F472392079YI083806362.993924e+1124HVZPXJDNCPNJKSFRKYJWGCRFMHEWHNDAMJGBNEMVOSKEG83182020-08-18798502B1384.200002418.408353YQZMVLXULA353AMJKFYSCAI15237PFHPJCVKOI860GEChemicals2020-081882020
WERKSNumMaterialCHARGAUFNRAUFPSLIFNRLICHAKUNNRVBELNPOSNRBUDATZEILEBWARTKZBEWMENGEMEINSEBELNEBELPKDAUFKDPOSMBLNRMJAHRPaisMAKTKFecha_Año_MesDiaMesAño
1414594IT99F881960394DM978904556.653090e+11399MDCBXVOECTLSBEWUHJVJCSEJUCHDZTBAVULTWEFQELWMG731072019-10-06354261F1322.738801137.500000VCCNQYSRLY9284WEIBLCFOCR8981QXGUFUGJDN163ITPlastic2019-106102019
1414595IT99F882734329EQ949336939.514849e+11219QFZBGBENYUAXNELNYQUXUJECBKFYHFTPEIGFCFENSWBGY896922019-04-25634261F2119.544702365.363935HLQBKVYENF2866VDBDWZOOKN12371HWUGXNVLPP412ITCardboard2019-042542019
1414596IT99F903916463JK852977827.716653e+11879ODAEDEOZTDECQPHBUPDNSRPFJDMGYZWBQNQBMGLAEBLDC911792022-02-25236261F114.975572107.952700VPEOIFZVJB4682MFBQIWERLV28360SFKOCNPREK497ITCardboard2022-022522022
1414597IT99F906372160PD435084926.028838e+11597DHFTIWZCOWAQOBSWMAZNYNXFRIGCYLDXNDKNFHVFOFYJJ643172019-09-23652261NaN759.973001070.894388JGZDPNEQBP6842TCOGOBRQIJ72743PZHRPQCMKE230ITPlastic2019-092392019
1414598IT99F918095308DN178192707.461717e+11445YRZWZWPQMZTTUXMQWRXUZWMUQWWKKYJSMPAKCQUGNDLZC328732019-06-13946261F249.500001913.767000QHFCYGMKIQ3308YZMFVAEOCL68268FMCDKYBTCA278ITWood2019-061362019
1414599IT99F933633093GH079249257.033192e+11414QEGDUHRXIQHDXDMDVSZPKTYMGNMLVIFNTZMBIJBBCBRRY645642019-04-21439261NaN1750.531902367.353000HRNYVMNQVY3103AWEIFSSMZA78721HHLUSWXBBP487ITFerrous Metal2019-042142019
1414601IT99F945131005EY220418297.626049e+11973ZZQNYQVNRGCZHLMRKAFHJPJIKTHDFEUEGLYGVSPSJSQUM325462022-08-02667261F1221.548502023.579000ZFSJYKXLIA2636DBAKGZCGUX93651AWFCKSZZUF862ITFerrous Metal2022-08282022
1414602IT99F951315432AQ744876106.267590e+11610DCJQTOHGCAQSGXWFRBPGBZRIJEIWMDSJOLPVTGEHSLAPD144002021-10-05607261F2339.824701382.244719MXMJNHZQTQ6320VMGMTWDBAQ71393JPXDPNGZVE602ITPaper2021-105102021
1414603IT99F977532645AI967191779.929810e+11630KDNMHUZEUJVTYORTELKWYAFYOTHRKFNSFOVIDSWHLKVPZ946572020-01-1255261F2290.200001577.568805XYAPOSYUXC6849PWAKIFWHNM7728KMBMNTYCXN286ITBiologics2020-011212020
1414604IT99F977586205QL231172634.555452e+10406ZUNOHODVWVAHFBSCSKQWOREGWDKAFFNWOGLPUHRHSFCOF100322019-10-21520261B1866.12540689.792251GEJESBGZTF7660MQCEMVECRR90078SMLNWSJALO408ITGlass2019-1021102019